arXiv Machine Learning July 24, 2026 By Yann Bouquet, Alireza Khodamoradi, Kristof Denolf, Mathieu Salzmann

KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers

Read the original on arXiv Machine Learning →

arXiv:2607. 21446v1 Announce Type: new Abstract: Post-training quantization (PTQ) of diffusion transformers (DiTs) to W4A4 severely degrades output quality, because activations entering each linear layer contain outliers that 4-bit formats cannot represent.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers

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arXiv:2601. 07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference.

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SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference

arXiv:2606. 26587v1 Announce Type: cross Abstract: Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation compression remains challenging: activations contain input-dependent outliers that dominate block scales in FP4 quantization, and directly applying N:M sparsity masks discards moderate values, coupling sparsification loss with quantization error.

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Model-Preserving Adaptive Rounding

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Related stories

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization

OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference

Model-Preserving Adaptive Rounding